Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5131-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5131-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning
Xudong Zhang
Key Laboratory of Ocean Observation and Forecasting, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Xiaofeng Li
CORRESPONDING AUTHOR
Key Laboratory of Ocean Observation and Forecasting, Qingdao, China
Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
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Cited
17 citations as recorded by crossref.
- Ocean temperature reconstruction from satellite observations in the North Atlantic using an explainable deep learning framework F. Liu et al. https://doi.org/10.1080/17538947.2026.2632430
- From Sparse Satellite Images to Continuous Visual Sequence: A Collaborative Diffusion Framework for Internal Solitary Waves Z. An et al. https://doi.org/10.1109/TGRS.2026.3689046
- Geometry-aware mapping with MI-IWNet reveals topographic modulation effects in internal wave evolution B. Xiao et al. https://doi.org/10.1016/j.jag.2026.105338
- Artificial intelligence for ocean phenomena forecasting X. Li et al. https://doi.org/10.1016/j.scib.2026.04.002
- Characterization of internal waves and their links to chlorophyll in the northern South China Sea (2003–2023) X. Hu & F. Liu https://doi.org/10.1080/01431161.2026.2672064
- Global open-ocean daily turbulent heat flux dataset (1992–2020) from SSM/I via deep learning H. Wang et al. https://doi.org/10.5194/essd-18-2929-2026
- Identification and Segmentation of Internal Solitary Waves in the East China Sea: A TransUNet Approach Using Multi-Source Satellite Imagery J. Xu et al. https://doi.org/10.3390/rs18010131
- CHGAFF-YOLO: A Cascade Hybrid Global Adaptive Feature Fusion Framework for Real-Time Ocean Internal Wave Detection X. Huo et al. https://doi.org/10.1109/LGRS.2025.3577597
- Automatic identification of internal solitary waves in numerical simulations based on an improved canny algorithm L. Chen et al. https://doi.org/10.1016/j.oceaneng.2025.123275
- An Enhanced SAR-Based ISW Detection Method Using YOLOv8 with an Anti-Interference Strategy and Repair Module and Its Applications Z. Lu et al. https://doi.org/10.3390/rs17193390
- Automatic Crest Line Extraction Algorithm for Internal Solitary Waves Based on SWOT P. Chen et al. https://doi.org/10.3390/rs18101463
- Internal solitary waves in the Banda Sea, a pathway between Indian and Pacific oceans: Satellite observations and physics-AI hybrid forecasting X. Zhang et al. https://doi.org/10.1016/j.rse.2025.114733
- IW-PPM: A BP neural network model with feature engineering for internal wave propagation prediction in the Northern South China Sea Z. Jiao et al. https://doi.org/10.1016/j.ocemod.2026.102746
- ProtoWave-Mamba: A Semi-Supervised Approach for Ocean Internal Wave Segmentation with Wavelet Enhancement and Long-Range Sequence Modeling K. Feng et al. https://doi.org/10.3390/rs18193360
- On the interaction between the nonlinear internal waves and the Pearl River plume W. Li et al. https://doi.org/10.1038/s41598-025-20724-0
- Ocean Rainfall Identification and Intensity Retrieval From GNSS-R Observations W. Deng et al. https://doi.org/10.1109/JSTARS.2026.3712747
- MSFF-CAA-ResNet: multi-scale and attention-enhanced residual network for tropical cyclone center location Z. Chen et al. https://doi.org/10.1007/s00343-025-5251-5
17 citations as recorded by crossref.
- Ocean temperature reconstruction from satellite observations in the North Atlantic using an explainable deep learning framework F. Liu et al. https://doi.org/10.1080/17538947.2026.2632430
- From Sparse Satellite Images to Continuous Visual Sequence: A Collaborative Diffusion Framework for Internal Solitary Waves Z. An et al. https://doi.org/10.1109/TGRS.2026.3689046
- Geometry-aware mapping with MI-IWNet reveals topographic modulation effects in internal wave evolution B. Xiao et al. https://doi.org/10.1016/j.jag.2026.105338
- Artificial intelligence for ocean phenomena forecasting X. Li et al. https://doi.org/10.1016/j.scib.2026.04.002
- Characterization of internal waves and their links to chlorophyll in the northern South China Sea (2003–2023) X. Hu & F. Liu https://doi.org/10.1080/01431161.2026.2672064
- Global open-ocean daily turbulent heat flux dataset (1992–2020) from SSM/I via deep learning H. Wang et al. https://doi.org/10.5194/essd-18-2929-2026
- Identification and Segmentation of Internal Solitary Waves in the East China Sea: A TransUNet Approach Using Multi-Source Satellite Imagery J. Xu et al. https://doi.org/10.3390/rs18010131
- CHGAFF-YOLO: A Cascade Hybrid Global Adaptive Feature Fusion Framework for Real-Time Ocean Internal Wave Detection X. Huo et al. https://doi.org/10.1109/LGRS.2025.3577597
- Automatic identification of internal solitary waves in numerical simulations based on an improved canny algorithm L. Chen et al. https://doi.org/10.1016/j.oceaneng.2025.123275
- An Enhanced SAR-Based ISW Detection Method Using YOLOv8 with an Anti-Interference Strategy and Repair Module and Its Applications Z. Lu et al. https://doi.org/10.3390/rs17193390
- Automatic Crest Line Extraction Algorithm for Internal Solitary Waves Based on SWOT P. Chen et al. https://doi.org/10.3390/rs18101463
- Internal solitary waves in the Banda Sea, a pathway between Indian and Pacific oceans: Satellite observations and physics-AI hybrid forecasting X. Zhang et al. https://doi.org/10.1016/j.rse.2025.114733
- IW-PPM: A BP neural network model with feature engineering for internal wave propagation prediction in the Northern South China Sea Z. Jiao et al. https://doi.org/10.1016/j.ocemod.2026.102746
- ProtoWave-Mamba: A Semi-Supervised Approach for Ocean Internal Wave Segmentation with Wavelet Enhancement and Long-Range Sequence Modeling K. Feng et al. https://doi.org/10.3390/rs18193360
- On the interaction between the nonlinear internal waves and the Pearl River plume W. Li et al. https://doi.org/10.1038/s41598-025-20724-0
- Ocean Rainfall Identification and Intensity Retrieval From GNSS-R Observations W. Deng et al. https://doi.org/10.1109/JSTARS.2026.3712747
- MSFF-CAA-ResNet: multi-scale and attention-enhanced residual network for tropical cyclone center location Z. Chen et al. https://doi.org/10.1007/s00343-025-5251-5
Saved (final revised paper)
Latest update: 28 Oct 2026
Short summary
Internal wave (IW) is an important ocean process and is frequently observed in the South China Sea (SCS). This study presents a detailed IW dataset for the northern SCS spanning from 2000 to 2022, with a spatial resolution of 250 m, comprising 3085 IW MODIS images. This dataset can enhance understanding of IW dynamics and serve as a valuable resource for studying ocean dynamics, validating numerical models, and advancing AI-driven model building, fostering further exploration into IW phenomena.
Internal wave (IW) is an important ocean process and is frequently observed in the South China...
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